Towards Emotion-Based Synthetic Consciousness: Using LLMs to Estimate Emotion Probability Vectors
This work addresses emotion analysis for AI applications, but it is incremental as it builds on existing LLM capabilities without achieving its broader goal.
The paper tackled the problem of estimating emotional states from text using LLMs, resulting in a method that maps emotion descriptors into a PCA-type space from Amazon product reviews, but failed to achieve action selection based on predicted emotional outcomes.
This paper shows how LLMs (Large Language Models) may be used to estimate a summary of the emotional state associated with piece of text. The summary of emotional state is a dictionary of words used to describe emotion together with the probability of the word appearing after a prompt comprising the original text and an emotion eliciting tail. Through emotion analysis of Amazon product reviews we demonstrate emotion descriptors can be mapped into a PCA type space. It was hoped that text descriptions of actions to improve a current text described state could also be elicited through a tail prompt. Experiment seemed to indicate that this is not straightforward to make work. This failure put our hoped for selection of action via choosing the best predict ed outcome via comparing emotional responses out of reach for the moment.